Detecting change in biological rhythms: a multivariate permutation test approach to Fourier-transformed data

Circulating inflammatory proteins associate with response to immune checkpoint inhibition therapy in patients with advanced melanoma

BACKGROUND

Inflammation can modulate tumour growth and progression, and influence clinical response to treatment. We investigated the potential of circulating inflammatory proteins for response stratification of immune checkpoint inhibitor (ICI) therapy for advanced melanoma.

METHODS

Study subjects were 87 patients with unresectable stage III or IV cutaneous melanoma from the multiple centres across the United Kingdom (UK) and the Netherlands (NL) who received ipilimumab, nivolumab, or pembrolizumab, or a combination of ipilimumab and nivolumab. Serum samples were collected before and during ICI therapy at follow-up visits scheduled every third week over a 12-week period. We performed targeted quantification of 92 proteins involved in inflammation and tested for association of their pre-treatment and on-treatment levels, as well as longitudinal changes, with overall response rate, progression-free survival, and overall survival.

FINDINGS

We observed consistently higher pre-treatment levels of interleukin-6 (IL-6), hepatocyte growth factor (HGF), and monocyte chemotactic protein 2 (MCP-2), in non-responders compared to responders (meta-analysis p=3.31 × 10^-4, 2.29 × 10^-4, and 1.02 × 10^-3, respectively). Patients' stratification according to the median value of IL-6, HGF, and MCP-2 highlighted a cumulative negative effect of pre-treatment levels of the three proteins on response (p=1.13 × 10^-2), with overall response rate among patients presenting with combined elevated IL-6, HGF, and MCP-2 levels being three-fold lower (26.7%) compared to patients with none of the three proteins elevated (80.0%, p=9.22 × 10^-3). Longitudinal data analysis showed that on-treatment changes in circulating inflammatory proteins are not correlated with response.

INTERPRETATION

Our findings are in line with an increasing body of evidence that the pro-inflammatory cytokine IL-6 can influence response to ICI in advanced melanoma, and further support a role of circulating HGF and MCP-2 levels as prognostic biomarkers as suggested by previous smaller studies. Inflammatory proteins may serve as predictive biomarkers of ICI response and valuable targets for combination therapy.

FUNDING

This work was supported by the Seerave Foundation and Dutch Cancer Society.

Risk assessment analysis for maternal autoantibody-related autism (MAR-ASD): a subtype of autism

The incidence of autism spectrum disorder (ASD) has been rising, however ASD-risk biomarkers remain lacking. We previously identified the presence of maternal autoantibodies to fetal brain proteins specific to ASD, now termed maternal autoantibody-related (MAR) ASD. The current study aimed to create and validate a serological assay to identify ASD-specific maternal autoantibody patterns of reactivity against eight previously identified proteins (CRMP1, CRMP2, GDA, NSE, LDHA, LDHB, STIP1, and YBOX) that are highly expressed in developing brain, and determine the relationship of these reactivity patterns with ASD outcome severity. We used plasma from mothers of children diagnosed with ASD (n = 450) and from typically developing children (TD, n = 342) to develop an ELISA test for each of the protein antigens. We then determined patterns of reactivity a highly significant association with ASD, and discovered several patterns that were ASD-specific (18% in the training set and 10% in the validation set vs. 0% TD). The three main patterns associated with MAR ASD are CRMP1 + GDA (ASD% = 4.2 vs. TD% = 0, OR 31.04, p = <0.0001), CRMP1 + CRMP2 (ASD% = 3.6 vs. TD% = 0, OR 26.08, p = 0.0005) and NSE + STIP1 (ASD% = 3.1 vs. TD% = 0, OR 22.82, p = 0.0001). Additionally, we found that maternal autoantibody reactivity to CRMP1 significantly increases the odds of a child having a higher Autism Diagnostic Observation Schedule (ADOS) severity score (OR 2.3; 95% CI: 1.358-3.987, p = 0.0021). This is the first report that uses machine learning subgroup discovery to identify with 100% accuracy MAR ASD-specific patterns as potential biomarkers of risk for a subset of up to 18% of ASD cases in this study population.

The impact of rainfall on drinking water quality in Antananarivo, Madagascar

Low-income cities that are subject to high population pressure and vulnerable to climate events often have a low capacity to continuously deliver safe drinking water. Here we reported the results of a 32-year survey on the temporal dynamics of drinking water quality indicators in the city of Antananarivo. We analyzed the long-term evolution of the quality of the water supplied and characterized the interactions between climatic conditions and the full-scale water supply system. A total of 25,467 water samples were collected every week at different points in the supplied drinking water system. Samples were analyzed for total coliforms (TC), Escherichia coli (EC), intestinal Enterococci (IE), and Spores of Sulphite-Reducing Clostridia (SSRC). Nine-hundred-eighty-one samples that were identified as positive for one or more indicators were unevenly distributed over time. The breakpoint method identified four periods when the time series displayed changes in the level and profile of contamination (i) and the monthly pattern of contamination (ii), with more direct effects of rainfall on the quality of supplied drinking water. The modeling showed significantly different lags among indicators of bacteria occurrence after cumulative rainfall, which range from 4 to 8 weeks. Among the effects of low-income urbanization, a rapid demographic transition and the degradation of urban watersheds have gradually affected the quality of the water supplied and resulted in the more direct effects of rainfall events. We focused on the need to adopt an alternative perspective of drinking water and urban watersheds management.

Precise periodic components estimation for chronobiological signals through Bayesian Inference with sparsity enforcing prior

The toxicity and efficacy of more than 30 anticancer agents present very high variations, depending on the dosing time. Therefore, the biologists studying the circadian rhythm require a very precise method for estimating the periodic component (PC) vector of chronobiological signals. Moreover, in recent developments, not only the dominant period or the PC vector present a crucial interest but also their stability or variability. In cancer treatment experiments, the recorded signals corresponding to different phases of treatment are short, from 7 days for the synchronization segment to 2 or 3 days for the after-treatment segment. When studying the stability of the dominant period, we have to consider very short length signals relative to the prior knowledge of the dominant period, placed in the circadian domain. The classical approaches, based on Fourier transform (FT) methods are inefficient (i.e., lack of precision) considering the particularities of the data (i.e., the short length). Another particularity of the signals considered in such experiments is the level of noise: such signals are very noisy and establishing the periodic components that are associated with the biological phenomena and distinguishing them from the ones associated with the noise are difficult tasks. In this paper, we propose a new method for the estimation of the PC vector of biomedical signals, using the biological prior informations and considering a model that accounts for the noise. The experiments developed in cancer treatment context are recording signals expressing a limited number of periods. This is a prior information that can be translated as the sparsity of the PC vector. The proposed method considers the PC vector estimation as an Inverse Problem (IP) using the general Bayesian inference in order to infer the unknown of our model, i.e. the PC vector but also the hyperparameters (i.e the variances). The sparsity prior information is modeled using a sparsity enforcing prior law. In this paper, we propose a Student's t distribution, viewed as the marginal distribution of a bivariate normal-inverse gamma distribution. We build a general infinite Gaussian scale mixture (IGSM) hierarchical model where we assign prior distributions also for the hyperparameters. The expression of the joint posterior law of the unknown PC vector and hyperparameters is obtained via Bayes rule, and then, the unknowns are estimated via joint maximum a posteriori (JMAP) or posterior mean (PM). For the PM estimator, the expression of the posterior distribution is approximated by a separable one, via variational Bayesian approximation (VBA), using the Kullback-Leibler (KL) divergence. For the PM estimation, two possibilities are considered: an approximation with a partially separable distribution and an approximation with a fully separable one. Both resulting algorithms corresponding to the PM estimation and the one corresponding to the JMAP estimation are iterative algorithms. The algorithms are presented in detail and are compared with the ones corresponding to the Gaussian model. We examine the convergency of the algorithms and give simulation results to compare their performances. Finally, we show simulation results on synthetic and real data in cancer treatment applications. The real data considered in this paper examines the rest-activity patterns of KI/KI Per2::luc mouse, aged 10 weeks, singly housed in RealTime Biolumicorder (RT-BIO).

Oscillatory serotonin function in depression

Oscillations in brain activities with periods of minutes to hours may be critical for normal mood behaviors. Ultradian (faster than circadian) rhythms of mood behaviors and associated central nervous system activities are altered in depression. Recent data suggest that ultradian rhythms in serotonin (5HT) function also change in depression. In two separate studies, 5HT metabolites in cerebrospinal fluid (CSF) were measured every 10 min for 24 h before and after chronic antidepressant treatment. Antidepressant treatments were associated with enhanced ultradian amplitudes of CSF metabolite levels. Another study used resting-state functional magnetic resonance imaging (fMRI) to measure amplitudes of dorsal raphé activation cycles following sham or active dietary depletions of the 5HT precursor (tryptophan). During depletion, amplitudes of dorsal raphé activation cycles increased with rapid 6 s periods (about 0.18 Hz) while functional connectivity weakened between dorsal raphé and thalamus at slower periods of 20 s (0.05 Hz). A third approach studied MDMA (ecstasy, 3,4-methylenedioxy-N-methylamphetamine) users because of their chronically diminished 5HT function compared with non-MDMA polysubstance users (Karageorgiou et al., 2009). Compared with a non-MDMA using cohort, MDMA users showed diminished fMRI intra-regional coherence in motor regions along with altered functional connectivity, again suggesting effects of altered 5HT oscillatory function. These data support a hypothesis that qualities of ultradian oscillations in 5HT function may critically influence moods and behaviors. Dysfunctional 5HT rhythms in depression may be a common endpoint and biomarker for depression, linking dysfunction of slow brain network oscillators to 5HT mechanisms affected by commonly available treatments. 5HT oscillatory dysfunction may define illness subtypes and predict responses to serotonergic agents. Further studies of 5HT oscillations in depression are indicated.

Cerebrospinal fluid sodium rhythms

Background

Cerebrospinal fluid (CSF) sodium levels have been reported to rise during episodic migraine. Since migraine frequently starts in early morning or late afternoon, we hypothesized that natural sodium chronobiology may predispose susceptible persons when extracellular CSF sodium increases. Since no mammalian brain sodium rhythms are known, we designed a study of healthy humans to test if cation rhythms exist in CSF.

Methods

Lumbar CSF was collected every ten minutes at 0.1 mL/min for 24 h from six healthy participants. CSF sodium and potassium concentrations were measured by ion chromatography, total protein by fluorescent spectrometry, and osmolarity by freezing point depression. We analyzed cation and protein distributions over the 24 h period and spectral and permutation tests to identify significant rhythms. We applied the False Discovery Rate method to adjust significance levels for multiple tests and Spearman correlations to compare sodium fluctuations with potassium, protein, and osmolarity.

Results

The distribution of sodium varied much more than potassium, and there were statistically significant rhythms at 12 and 1.65 h periods. Curve fitting to the average time course of the mean sodium of all six subjects revealed the lowest sodium levels at 03.20 h and highest at 08.00 h, a second nadir at 09.50 h and a second peak at 18.10 h. Sodium levels were not correlated with potassium or protein concentration, or with osmolarity.

Conclusion

These CSF rhythms are the first reports of sodium chronobiology in the human nervous system. The results are consistent with our hypothesis that rising levels of extracellular sodium may contribute to the timing of migraine onset. The physiological importance of sodium in the nervous system suggests that these rhythms may have additional repercussions on ultradian functions.